Discrimination between malignant and benign thyroid tumors by diffusion-weighted imaging – A systematic review and meta analysis

PurposeMagnetic resonance imaging is used to stage thyroid tumors. Diffusion weighted imaging (DWI) and apparent diffusion coefficient (ADC) can be used to reflect tumor microstructure. Our aim was to compare ADC values of malignant and benign thyroid lesions based on a large sample.MethodsMEDLINE library, EMBASE and SCOPUS databases were screened for the associations between ADC values and thyroid lesions up to August 2021. The primary endpoint of the systematic review were ADC values of benign and malignant thyroid lesions. In total, 29 studies were suitable for the analysis and were included into the present study.ResultsThe included studies comprised a total of 2137 lesions, 1118 (52.3%) benign and 1019 (47.7%) malignant lesions. The pooled mean ADC value of the benign thyroid lesions was 1.88 × 10⁻³ mm²/s [95% CI 1.77–2.0] and the pooled mean ADC value of malignant thyroid lesions was 1.15 × 10⁻³ mm²/s [95% CI 1.04–1.25].ConclusionsADC can well discriminate benign and malignant thyroid tumors. Therefore, DWI should be implemented into the presurgical diagnostic work-up in clinical routine.     

Free-breathing radial volumetric interpolated breath-hold examination sequence and dynamic contrast-enhanced MRI combined with diffusion-weighted imaging for assessment of solitary pulmonary nodules

ObjectiveTo test the performance of free-breathing Dynamic Contrast-Enhanced MRI (DCE-MRI) using a radial volumetric interpolated breath-hold examination (VIBE) sequence combined with diffusion-weighted imaging (DWI) for quantitative solitary pulmonary nodule (SPN) assessment.MethodsA total of 67 SPN cases receiving routine MRI routine scans, DWI, and dynamic-enhanced MRI in our hospital from May 2017 to November 2018 were collected. These cases were divided into a malignant group and a benign group according to the characteristics of the SPNs. The quantitative DCE-MRI parameters (K^trans, K_ep, V_e) and apparent diffusion coefficient (ADC) values of the nodules were measured.ResultsThe K^trans and K_ep values in the malignant group were higher than those in the benign group, while the ADC values in the malignant group were lower than those in the benign group. Furthermore, the K^trans value of adenocarcinoma was higher than that of squamous cell carcinoma and small cell carcinoma (P < 0.05). The V_e value was significantly different between non-small cell carcinoma and small cell carcinoma (P < 0.05). With an ADC value of 0.98 × 10⁻³ mm²/s as the threshold, the specificity and sensitivity to diagnose benign and malignant nodules was 90.6% and 80%, respectively.ConclusionHigh-temporal-resolution DCE-MRI using the r-VIBE technique in combination with DWI could contribute to pulmonary nodule analysis and possibly serve as a potential alternative to distinguish malignant from benign nodules as well as differentiate different types of malignancies.     

Can diffusion-weighted imaging be used as a reliable sequence in the detection of malignant pulmonary nodules and masses?

Recent developments in diffusion-weighted magnetic resonance imaging (DWI) make it possible to image malignant tumors to provide tissue contrast based on difference with the diffusion of water molecules among tissues, which can be measured by the apparent diffusion coefficient (ADC) value. We aimed to assess the diagnostic accuracy of DWI for benign/malignant discrimination of pulmonary nodules/masses with a meta-analysis. The MEDLINE, EMBASE, Cancerlit and Cochrane Library database, from January 2001 to August 2011, were searched for studies evaluating the diagnostic accuracy of DWI for benign/malignant discrimination of pulmonary nodules. We determined sensitivities and specificities across studies, calculated positive and negative likelihood ratios (LRP and LRN), and constructed summary receiver operating characteristic SROC) curves. Across 10 studies (545 patients), there was no evidence of publication bias (P= .22, bias=−19.19). DWI had a pooled sensitivity of 0.84 (95% CI, 0.76–0.90) and a pooled specificity of 0.84 (95% CI, 0.64–0.94). Overall, LRP was 5.3 (95% CI, 2.1–13.0) and LRN was 0.19 (95% CI, 0.12–0.30). In patients with high pretest probabilities, DWI enabled confirmation of malignant pulmonary lesion; in patients with low pretest probabilities, DWI enabled exclusion of malignant pulmonary lesion. Worst-case-scenario (pretest probability, 50%) posttest probabilities were 84% and 16% for positive and negative DWI results, respectively. Diffusion-weighted magnetic resonance imaging can be used to differentiate malignant from benign pulmonary lesions. High-quality prospective studies regarding DWI in the evaluation of pulmonary nodules are still needed to be conducted.     

A multiparametric approach to diagnosing breast lesions using diffusion-weighted imaging and ultrafast dynamic contrast-enhanced MRI

PurposeTo evaluate the diagnostic performance of a multiparametric approach to breast lesions including apparent diffusion coefficient (ADC) from diffusion-weighted images (DWI), maximum slope (MS) from ultrafast dynamic contrast enhanced (UF-DCE) MRI, lesion size, and patient's age.Materials and methodsIn total, 96 lesions (73 malignant, 23 benign) were evaluated. UF-DCE MRI was acquired using a prototype 3D-gradient-echo volumetric interpolated breath-hold examination (VIBE) with compressed sensing. Images were obtained up to 1 min after gadolinium injection. MS was calculated as the percentage relative enhancement/s. An ADC map was automatically generated from DWI at b = 0 and b = 1000 s/mm². MS and ADC values were measured by two radiologists independently. Interrater agreement was evaluated using intraclass correlation coefficients. Univariate and multivariate logistic regression analyses were performed using MS, ADC, lesion size, and the patient's age. The parameters of the prediction model were generated from the results of the multivariate logistic regression analysis. Area under the curve (AUC) was used to compare diagnostic performance of the prediction model and each parameter.ResultsInterrater agreements on MS and ADC were excellent (ICC 0.99 and 0.88, respectively). MS, ADC, and patient's age remained as significant parameters after univariate and multivariate logistic regression analysis. The prediction model using these significant parameters yielded an AUC of 0.90, significantly higher than that of MS (AUC 0.74, p = 0.01). The AUCs of ADC, MS, patient's age were 0.87, 0.74 and 0.73, respectively.ConclusionsA multiparametric model using ADC from DWI, MS from UF-DCE MRI, and patient's age showed excellent diagnostic performance, with greater contribution of ADC. Combining DWI and UF-DCE MRI might reduce scanning time while preserving diagnostic performance.     

A knowledge-driven feature learning and integration method for breast cancer diagnosis on multi-sequence MRI

BackgroundThe classification of benign versus malignant breast lesions on multi-sequence Magnetic Resonance Imaging (MRI) is a challenging task since breast lesions are heterogeneous and complex. Recently, deep learning methods have been used for breast lesion diagnosis with raw image input. However, without the guidance of domain knowledge, these data-driven methods cannot ensure that the features extracted from images are comprehensive for breast cancer diagnosis. Specifically, these features are difficult to relate to clinically relevant phenomena.PurposeInspired by the cognition process of radiologists, we propose a Knowledge-driven Feature Learning and Integration (KFLI) framework, to discriminate between benign and malignant breast lesions using Multi-sequences MRI.MethodsStarting from sequence division based on characteristics, we use domain knowledge to guide the feature learning process so that the feature vectors of sub-sequence are constrained to lie in characteristic-related semantic space. Then, different deep networks are designed to extract various sub-sequence features. Furthermore, a weighting module is employed for the integration of the features extracted from different sub-sequence images adaptively.ResultsThe KFLI is a domain knowledge and deep network ensemble, which can extract sufficient and effective features from each sub-sequence for a comprehensive diagnosis of breast cancer. Experiments on 100 MRI studies have demonstrated that the KFLI achieves sensitivity, specificity, and accuracy of 84.6%, 85.7% and 85.0%, respectively, which outperforms other state-of-the-art algorithms.     

Differentiation of bone metastases from prostate cancer and benign red marrow depositions of the pelvic bone with multiparametric MRI

PurposeTo investigate the diagnostic utilities of imaging parameters derived from T1-weighted imaging (T1WI), diffusion-weighted imaging (DWI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to differentiate bone metastases from prostate cancer and benign red marrow depositions of the pelvic bone.Materials and methodsThirty-six lesions from 36 patients with prostate cancer were analyzed with T1WI, DWI, and DCE-MRI. The lesions were classified in the bone metastases (n = 22) and benign red marrow depositions (n = 14). Lesion-muscle ratio (LMR), apparent diffusion coefficient (ADC), volume transfer constant (K^trans), reflux rate (Kep), and volume fraction of the extravascular extracellular matrix (Ve) values were obtained from the lesions. The imaging parameters of the both groups were compared using the Mann-Whitney U test, receiver operating characteristics (ROC) curves were analyzed. For the ROC curves, area under the curves (AUCs) were compared.ResultsThe ADC, K^trans, K_ep, and V_e values of bone metastases were significantly higher than those of benign red marrow depositions (Mann-Whitney U test, p < 0.05). However, there was no significant difference in LMR between the two groups (Mann-Whitney U test, p = 0.360). The AUCs of K^trans_, K_ep, ADC, V_e, and LMR were 0.896, 0.844, 0.812, 0.724, and 0.448, respectively. In the pairwise comparison of ROC curves, the AUCs of K^trans and K_ep was significantly higher than LMR.ConclusionsK^trans, K_ep, V_e, and ADC values can be used as imaging tools to differentiate bone metastases from prostate cancer and benign red marrow depositions of the pelvic bone.     

Deep-learning approach with convolutional neural network for classification of maximum intensity projections of dynamic contrast-enhanced breast magnetic resonance imaging

PurposeWe aimed to evaluate deep learning approach with convolutional neural networks (CNNs) to discriminate between benign and malignant lesions on maximum intensity projections of dynamic contrast-enhanced breast magnetic resonance imaging (MRI).MethodsWe retrospectively gathered maximum intensity projections of dynamic contrast-enhanced breast MRI of 106 benign (including 22 normal) and 180 malignant cases for training and validation data. CNN models were constructed to calculate the probability of malignancy using CNN architectures (DenseNet121, DenseNet169, InceptionResNetV2, InceptionV3, NasNetMobile, and Xception) with 500 epochs and analyzed that of 25 benign (including 12 normal) and 47 malignant cases for test data. Two human readers also interpreted these test data and scored the probability of malignancy for each case using Breast Imaging Reporting and Data System. Sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) were calculated.ResultsThe CNN models showed a mean AUC of 0.830 (range, 0.750–0.895). The best model was InceptionResNetV2. This model, Reader 1, and Reader 2 had sensitivities of 74.5%, 72.3%, and 78.7%; specificities of 96.0%, 88.0%, and 80.0%; and AUCs of 0.895, 0.823, and 0.849, respectively. No significant difference arose between the CNN models and human readers (p > 0.125).ConclusionOur CNN models showed comparable diagnostic performance in differentiating between benign and malignant lesions to human readers on maximum intensity projection of dynamic contrast-enhanced breast MRI.     

In vivo multiparametric magnetic resonance imaging study for differentiating the severity of hepatic warm ischemia-reperfusion injury in a rabbit model

ObjectivesTo assess the value of multiparametric magnetic resonance imaging including intravoxel incoherent motion (IVIM), diffusion tensor imaging (DTI) and blood oxygen level dependent (BOLD) MRI in differentiating the severity of hepatic warm ischemia-reperfusion injury (WIRI) in a rabbit model.MethodsFifty rabbits were randomly divided into a sham-operation group and four test groups (n = 10 for each group) according to different hepatic warm ischemia times. IVIM, DTI and BOLD MRI were performed on a 3 T MR scanner with 11 b values (0 to 800 s/mm²), 2 b values (0 and 500 s/mm²) on 12 diffusion directions, multiple-echo gradient echo (GRE) sequences (TR/TE, 75/2.57–24.25 ms), respectively. IVIM, DTI and BOLD MRI parameters, hepatic biochemical and histopathological parameters were compared. Pearson and Spearman correlation methods were performed to assess the correlation between these MRI parameters and laboratory parameters. Furthermore, receiver operating characteristic (ROC) curves were compiled to determine diagnostic efficacies.ResultsTrue diffusion (Dslow), pseudodiffusion (Dfast), perfusion fraction (PF), mean diffusivity (MD) significantly decreased, while R2* significantly increased with prolonged warm ischemia times, and significant differences were found in all of biochemical and histopathological parameters (all P < 0.05). Dslow, PF, and R2* correlated significantly with all of biochemical and histopathological parameters (all |r| = 0.381–0.746, all P < 0.05). ROC analysis showed that the area under the ROC curve (AUC) of IVIM across hepatic WIRI groups was the largest among IVIM, DTI and BOLD.ConclusionsMultiparametric MRI may be helpful with characterization of early changes and determination of severity of hepatic WIRI in a rabbit model.     

Intravoxel incoherent motion diffusion-weighted imaging as an adjunct to dynamic contrast-enhanced MRI to improve accuracy of the differential diagnosis of benign and malignant breast lesions

PurposeTo investigate the value of use of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) as an adjunct to dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to distinguish benign from malignant breast lesions.Materials and methodsRetrospective analysis of data pertaining to 117 patients with breast lesions who underwent DCE-MRI and IVIM-DWI examination with 3.0 T MRI was conducted. A total of 128 lesions were pathologically confirmed (47 benign and 81 malignant). Between-group differences in DCE-MRI parameters (Morphology, enhancement pattern, maximum slope of increase (MSI) and time–signal curve (TIC) type) and IVIM-DWI parameters (f value, D value and D* value) were assessed. Multivariate logistic regression was performed to identify variables that distinguished benign from malignant breast lesions. The diagnostic performance of DCE-MRI and DCE-MRI plus IVIM-DWI, to distinguish benign from malignant breast lesions, was evaluated using pathology results as the gold standard.ResultsLesion morphology, MSI, and TIC type (P < 0.05), but not the enhancement pattern (P > 0.05), were significantly different between the benign and malignant groups. The f (8.53 ± 2.14) and D* (7.64 ± 2.07) values in the malignant group were significantly higher than those in the benign group (7.68 ± 1.97 and 6.83 ± 2.13, respectively), while the D value (0.99 ± 0.22) was significantly lower than that (1.34 ± 0.17) in the benign group (P < 0.05 for all). On logistic regression analysis, the sensitivity, specificity and accuracy of DCE-MRI were 90.1%, 70.2% and 82.8% respectively; the corresponding figures for the combination of IVIM-DWI and DCE-MRI were 88.8%, 85.1%, and 87.5%respectively.ConclusionIVIM-DWI method as an adjunct to DCE-MRI can improve the specificity and accuracy in differential diagnosis of benign and malignant lesions of breast.     

Investigating brain tumor differentiation with diffusion and perfusion metrics at 3T MRI using pattern recognition techniques

The aim of this study was to evaluate the contribution of diffusion and perfusion MR metrics in the discrimination of intracranial brain lesions at 3T MRI, and to investigate the potential diagnostic and predictive value that pattern recognition techniques may provide in tumor characterization using these metrics as classification features. Conventional MRI, diffusion weighted imaging (DWI), diffusion tensor imaging (DTI) and dynamic-susceptibility contrast imaging (DSCI) were performed on 115 patients with newly diagnosed intracranial tumors (low-and- high grade gliomas, meningiomas, solitary metastases). The Mann–Whitney U test was employed in order to identify statistical differences of the diffusion and perfusion parameters for different tumor comparisons in the intra-and peritumoral region. To assess the diagnostic contribution of these parameters, two different methods were used; the commonly used receiver operating characteristic (ROC) analysis and the more sophisticated SVM classification, and accuracy, sensitivity and specificity levels were obtained for both cases. The combination of all metrics provided the optimum diagnostic outcome. The highest predictive outcome was obtained using the SVM classification, although ROC analysis yielded high accuracies as well. It is evident that DWI/DTI and DSCI are useful techniques for tumor grading. Nevertheless, cellularity and vascularity are factors closely correlated in a non-linear way and thus difficult to evaluate and interpret through conventional methods of analysis. Hence, the combination of diffusion and perfusion metrics into a sophisticated classification scheme may provide the optimum diagnostic outcome. In conclusion, machine learning techniques may be used as an adjunctive diagnostic tool, which can be implemented into the clinical routine to optimize decision making.     

Comparison of accuracy of intravoxel incoherent motion and apparent diffusion coefficient techniques for predicting malignancy of head and neck tumors using half-Fourier single-shot turbo spin-echo diffusion-weighted imaging

Purpose

To evaluate the use of the intravoxel incoherent motion (IVIM) technique in half-Fourier single-shot turbo spin-echo (HASTE) diffusion-weighted imaging (DWI), and to compare its accuracy to that of apparent diffusion coefficient (ADC) to predict malignancy in head and neck tumors.

Patients and methods

HASTE DW images of 33 patients with head and neck tumors (10 benign and 23 malignant) were evaluated. Using the IVIM technique, parameters (D, true diffusion coefficient; f, perfusion fraction; D*, pseudodiffusion coefficient) were calculated for each tumor. ADC values were measured over a range of b values from 0 to 1000 s/mm². IVIM parameters and ADC values in benign and malignant tumors were compared using Student's t test, receiver operating characteristics (ROC) analysis, and multivariate logistic regression modeling.

Results

Mean ADC and D values of malignant tumors were significantly lower than those of benign tumors (P < 0.05). Mean D* values of malignant tumors were significantly higher than those of benign tumors (P < 0.05). There was no significant difference in mean f values between malignant and benign tumors (P > 0.05). The technique of combining D and D* was the best for predicting malignancy; accuracy for this model was higher than that for ADC.

Conclusions

The IVIM technique may be applied in HASTE DWI as a diagnostic tool to predict malignancy in head and neck masses. The use of D and D* in combination increases the diagnostic accuracy in comparison with ADC.     

Diffusion weighted imaging for evaluation of breast lesions: Comparison between high b-value single-shot and routine readout-segmented sequences at 3 T

PurposeIn this study, we compare readout-segmented echo-planar imaging (rs-EPI) Diffusion Weighted Imaging (DWI) to a work-in-progress single-shot EPI with modified Inversion Recovery Background Suppression (ss-EPI-mIRBS) sequence at 3 T using a b-value of 2000 s/mm² on image quality, lesion visibility and evaluation time.MethodFrom September 2017 to December 2018, 23 women (one case used for training) with known breast cancer were included in this study, after providing signed informed consent. Women were scanned with the conventional rs-EPI sequence and the work-in-progress ss-EPI-mIRBS during the same examination. Four breast radiologists (4–13 years of experience) independently scored both series for overall image quality (1: extremely poor to 9: excellent). All lesions (47 in total, 36 malignant, and 11 benign and high-risk) were evaluated for visibility (1: not visible, 2: visible if location is given, 3: visible) and probability of malignancy (BI-RADS 1 to 5). ADC values were determined by measuring signal intensity in the lesions using dynamic contrast-enhanced (DCE) images for reference. Evaluation times for all assessments were automatically recorded. Results were analyzed using the visual grading characteristics (VGC) and the resulting area under the curve (AUC_VGC) method. Statistical analysis was performed in SPSS, with McNemar tests, and paired t-tests used for comparison.ResultsNo significant differences were detected between the two sequences in image quality (AUC_VGC: 0.398, p = 0.087) and lesion visibility (AUC_VGC: 0.534, p = 0.336) scores. Lesion characteristics (e.g benign and high-risk, versus malignant; small (≤10 mm) vs. larger (>10 mm)) did not result in different image quality or lesion visibility between sequences. Sensitivity (rs-EPI: 72.2% vs. ss-EPImIRBS: 78.5%, p = 0.108) and specificity (70.5% vs. 56.8%, p = 0.210, respectively) were comparable. In both sequences the mean ADC value was higher for benign and high-risk lesions than for malignant lesions (ss-EPI-mIRBS: p = 0.022 and rs-EPI: p = 0.055). On average, ss-EPI-mIRBS resulted in decreased overall reading time by 7.7 s/case (p = 0.067); a reduction of 17%. For malignant lesions, average reading time was significantly shorter using ss-EPI-mIRBS compared to rs-EPI (64.0 s/lesion vs. 75.9 s/lesion, respectively, p = 0.039).ConclusionBased on this study, the ss-EPI sequence using a b-value of 2000 s/mm² enables for a mIRBS acquisition with quality and lesion conspicuity that is comparable to conventional rs-EPI, but with a decreased reading time.     

In vivo hypoxia characterization using blood oxygen level dependent magnetic resonance imaging in a preclinical glioblastoma mouse model

PurposeHypoxia measurements can provide crucial information regarding tumor aggressiveness, however current preclinical approaches are limited. Blood oxygen level dependent (BOLD) Magnetic Resonance Imaging (MRI) has the potential to continuously monitor tumor pathophysiology (including hypoxia). The aim of this preliminary work was to develop and evaluate BOLD MRI followed by post-image analysis to identify regions of hypoxia in a murine glioblastoma (GBM) model.MethodsA murine orthotopic GBM model (GL261-luc2) was used and independent images were generated from multiple slices in four different mice. Image slices were randomized and split into training and validation cohorts. A 7 T MRI was used to acquire anatomical images using a fast-spin-echo (FSE) T2-weighted sequence. BOLD images were taken with a T2*-weighted gradient echo (GRE) and an oxygen challenge. Thirteen images were evaluated in a training cohort to develop the MRI sequence and optimize post-image analysis. An in-house MATLAB code was used to evaluate MR images and generate hypoxia maps for a range of thresholding and ΔT2* values, which were compared against respective pimonidazole sections to optimize image processing parameters. The remaining (n = 6) images were used as a validation group. Following imaging, mice were injected with pimonidazole and collected for immunohistochemistry (IHC). A test of correlation (Pearson's coefficient) and agreement (Bland-Altman plot) were conducted to evaluate the respective MRI slices and pimonidazole IHC sections.ResultsFor the training cohort, the optimized parameters of “thresholding” (20 ≤ T2* ≤ 35 ms) and ΔT2* (±4 ms) yielded a Pearson's correlation of 0.697. These parameters were applied to the validation cohort confirming a strong Pearson's correlation (0.749) when comparing the respective analyzed MR and pimonidazole images.ConclusionOur preliminary study supports the hypothesis that BOLD MRI is correlated with pimonidazole measurements of hypoxia in an orthotopic GBM mouse model. This technique has further potential to monitor hypoxia during tumor development and therapy.     

Advanced diffusion models in head and neck squamous cell carcinoma patients: Goodness of fit,relationships among diffusion parameters and comparison with dynamic contrast-enhanced perfusion

PurposeWe assessed advanced fitting models of diffusion weighted imaging (DWI) in head/neck squamous cell carcinoma (HNSCC) patients to determine the best goodness of fit and correlations among diffusion parameters. We compared these results with those of dynamic contrast-enhanced (DCE) perfusion parameters.Materials and methodsWe retrospectively evaluated 32 HNSCC patients (12 sinonasal, 20 pharynx/oral cavity). The DWI acquisition used single-shot spin-echo echo-planar imaging (EPI) with 12 b-values (0 − 2000). We calculated 14 DWI parameters using mono-exponential, bi-exponential, and tri-exponential models, stretched exponential model (SEM) and diffusion kurtosis imaging (DKI) models. We compared each model's goodness of fit using the residual sum of squares (RSS), Akaike Information Criterion (AIC) and Bayesian information criterion (BIC) value. We determined the correlation between each pair of DWI parameters and between each DWI parameter and DCE perfusion parameter.ResultsThe tri-exponential fit's RSS, AIC and BIC values were significantly smaller than those for bi-exponential fit. The RSS, AIC and BIC values of the SEM fit and DKI fit were significantly smaller than mono-exponential model. Significant correlations were observed in 30 pairs (sinonasal cavity) and 31 (sinonasal cavity group) among 91 DWI parameter combinations. Significant correlations were also observed in nine pairs (both sinonasal cavity and pharynx/oral cavity group) among 64 DWI/DCE perfusion parameter pairs, in particular, high positive correlations between the tri-exponential model's intermediate diffusion fraction (f₂) and the volume of the extracellular extravascular space per unit volume of tissue (v_e) were observed in both patient groups.ConclusionWe identified several correlations between DWI parameters by advanced fitting models and correlations between DWI and DCE parameters. These will help determine HNSCC patients' detailed tissue structures.     

Imaging focal reperfusion injury following global ischemia with diffusion-weighted magnetic resonance imaging and 1H-Magnetic Resonance Spectroscopy

The purpose of the study was to determine whether diffusion-weighted magnetic resonance imaging (DWI) could identify focal lesions that develop in ischemia-sensitive cerebral tissues during reperfusion following global brain ischemia. Localized ¹H-Magnetic Resonance Spectroscopy (¹H-MRS) measurements were also obtained to determine whether abnormal spectroscopic markers were associated with focal lesions and to define time correlations between DWI and metabolic changes. Brain diffusion-weighted magnetic resonance imaging measurements were made in a cat model of repetitive global cerebral ischemia and reperfusion. Five animals were exposed to three episodes of 10 min vascular occlusions at hourly intervals. Three animals were evaluated as controls. DWI, T2WI, and ¹H-MRS data were acquired for up to 12 h. Transient focal DWI hyperintensity was detected in the hippocampus, basal ganglia, and cortical watershed areas. These focal abnormalities usually appeared during the final reperfusion and eventually spread to encompass all of the gray matter. Spectroscopic measurements demonstrated the expected elevation of the lactate signal intensity during vessel occlusion, which returned to normal during early reperfusion. A subsequent rise in the lactate signal occurred approximately 3–4 h after the beginning of the third reperfusion. This late lactate elevation occurred after focal hyperintensities were identified by DWI. No significant signal changes were seen in spectroscopic metabolites other than lactate. The study illustrates that DWI and ¹H-MRS are sensitive to focal cerebral lesions that occur during reperfusion following global cerebral ischemia.     

Pediatric abdominal masses: diagnostic accuracy of diffusion weighted MRI

Purpose

To retrospectively identify apparent diffusion coefficient (ADC) values of pediatric abdominal mass lesions, to determine whether measured ADC of the lesions and signal intensity on diffusion-weighted (DW) images allow discrimination between benign and malignant mass lesions.

Materials and Methods

Approval for this retrospective study was obtained from the institutional review board. Children with abdominal mass lesions, who were examined by DW magnetic resonance imaging (MRI) were included in this study. DW MR images were obtained in the axial plane by using a non breath-hold single-shot spin-echo sequence on a 1.5-T MR scanner. ADCs were calculated for each lesion. ADC values were compared with Mann–Whitney U test. Receiver operating characteristic curve analysis was performed to determine cut-off values for ADC. The results of visual assessment on b800 images and ADC map images were compared with chi-square test.

Results

Thirty-one abdominal mass lesions (16 benign, 15 malignant) in 26 patients (15 girls, 11 boys, ranging from 2 days to 17 years with 6.9 years mean) underwent MRI. Benign lesions had significantly higher ADC values than malignant ones (P<.001). The mean ADCs of malignant lesions were 0.84±1.7×10⁻³ mm²/s, while the mean ADCs of the benign ones were 2.28±1.00×10⁻³ mm²/s. With respect to cutoff values of ADC: 1.11×10⁻³ mm²/s, sensitivity and negative predictive values were 100%, specificity was 78.6% and positive predictive value was 83.3%. For b800 and ADC map images, there were statistically significant differences on visual assessment. All malignant lesions had variable degrees of high signal intensity whereas eight of the 16 benign ones had low signal intensities on b800 images (P<.001). On ADC map images, all malignant lesions were hypointense and most of the benign ones (n=11, 68.7%) were hyperintense (P<.001).

Conclusion

DW imaging can be used for reliable discrimination of benign and malignant pediatric abdominal mass lesions based on considerable differences in the ADC values and signal intensity changes.     

Handcrafted MRI radiomics and machine learning: Classification of indeterminate solid adrenal lesions

PurposeTo assess a radiomic machine learning (ML) model in classifying solid adrenal lesions (ALs) without fat signal drop on chemical shift (CS) as benign or malignant.Method55 indeterminate ALs (21 lipid poor adenomas, 15 benign pheocromocytomas, 1 oncocytoma, 12 metastases, 6 primary tumors) showing no fat signal drop on CS were retrospectively included. Manual 3D segmentation on T2-weighted and CS images was performed for subsequent radiomic feature extraction. After feature stability testing and an 80–20% train-test split, the train set was balanced via oversampling. Following a multi-step feature selection, an Extra Trees model was tuned with 5-fold stratified cross-validation in the train set and then tested on the hold-out test set.ResultsA total of 3396 features were extracted from each AL, of which 133 resulted unstable while none had low variance (< 0.01). Highly correlated (r > 0.8) features were also excluded, leaving 440 parameters. Among these, Support Vector Machine 5-fold stratified cross-validated recursive feature elimination selected a subset of 6 features. ML obtained a cross-validation accuracy of 0.94 on the train and 0.91 on the test sets. Precision, recall and F1 score were respectively 0.92, 0.91 and 0.91.ConclusionsOur MRI handcrafted radiomics and ML pipeline proved useful to characterize benign and malignant solid indeterminate adrenal lesions.     

Triple-negative breast cancer: Pretreatment magnetic resonance imaging features and clinicopathological factors associated with recurrence

PurposeWe aimed to investigate the magnetic resonance imaging (MRI) features and clinicopathologic factors with recurrence of triple-negative breast cancer (TNBC).Patients and methodsWe identified 281 patients with 288 surgically confirmed TNBC lesions who underwent pretreatment MRI between 2009 and 2015. The presence of intratumoral high signal on T2-weighted images, high-signal rim on diffusion-weighted images (DWI), and rim enhancement on the dynamic contrast-enhanced MRI and clinicopathological data were collected. Cox proportional analysis was performed.ResultsOf the 288 lesions, 36 (12.5%) recurred after a median follow-up of 18 months (range, 3.6–68.3 months). Rim enhancement (hazard ratio [HR] = 3.15; 95% confidence interval [CI] = 1.01, 9.88; p = .048), and lymphovascular invasion (HR = 2.73, 95% CI = 1.20, 6.23; p = .016) were independently associated with disease recurrence. While fibroglandular volume, background parenchymal enhancement, intratumoral T2 high signal, and high-signal rim on DWI, were not found to be risk factors for recurrence.ConclusionPretreatment MRI features may help predict a high risk of recurrence in patients with TNBC.     

Intravoxel incoherent motion magnetic resonance imaging for breast cancer: A comparison with molecular subtypes and histological grades

PurposeThe purpose of this paper is to investigate whether the IVIM parameters (D, D *, f) helps to determine the molecular subtypes and histological grades of breast cancer.MethodsFifty-one patients with breast cancer were included in the study. All subjects were examined by 3 T Magnetic Resonance Imaging (MRI). Diffusion-weighted imaging (DWI) was undertaken with 16 b-values. IVIM parameters [D (true diffusion coefficient), D* (pseudo-diffusion coefficient), f (perfusion fraction)] were calculated. Histopathological reports were reviewed to histological grade, histological type, and immunohistochemistry. IVIM parameters of tumors with different histological grades and molecular subtypes were compared.ResultsD* and f were significantly different between molecular subtypes (p = 0.019, p = 0.03 respectively). D* and f were higher in the HER-2 group and lower in Triple negative (−) group (D*:36.8 × 10⁻³ ± 5.3 × 10⁻³ mm²/s, f:29.5%, D*:29.8 × 10⁻³ ± 5.6 × 10⁻³ mm²/s, f:21.5% respectively). There was a significant difference in D* and f between HER-2 and Triple (−) subgroups (p = 0,028, p = 0.024, respectively). D* was also significantly different between the HER-2 group and the Luminal group (p = 0,041). While histological grades increase, D and f values tend to decrease, and D* tends to increase. While the Ki-67 index increases, D* and f values tend to increase, and D tend to decrease.ConclusionD* and f values measured with IVIM imaging were useful for assessing breast cancer molecular subtyping. IVIM imaging may be an alternative to breast biopsy for sub-typing of breast cancer with further research.